Bacterial infection remains a major cause of suffering and death, particularly in patients with impaired host defence. Although there is extensive knowledge on the mechanisms, pathways, mediators, transcription factors, receptor levels and gene activation involved in the host response to severe infection, which may lead to organ dysfunction, the understanding of the whole system working in concert typically has limitations.
In the clinical setting, current monitoring techniques have achieved a high level of sophistication, involving vital sign monitoring, labs, and a variety of radiology, microbiology and pathology tests. Although these tests are generally adequate to reliably diagnose infection, the criteria to diagnose infection are non-specific. Frequently, a gestalt of individually non-specific clinical signs and symptoms lead to the diagnosis of infection and initiation of antibiotic therapy. As such, the timing of diagnosis is imprecise, insensitive and subject to judgement, which may lead to delay. In certain patient populations with increased susceptibility or impaired reserve, the delay in diagnosis, even if measured in hours, may prove catastrophic. Clinical deterioration may be well underway prior to recognition and response. Late diagnosis of infection, rapid clinical deterioration, ICU admission and organ dysfunction are not uncommon in the case histories of critically ill patients.
For example, severe sepsis and septic shock are the most common causes of mortality in critically ill patients, accounting for 10% of intensive care unit admissions (Brun-Buisson C. The epidemiology of the systemic inflammatory response. Intensive Care Med. 2000; 26 Suppl 1:S64-74) and 2.9% of all hospital admissions (Angus D C, Linde-Zwirble W T, Lidicker J, Clermont G, Carcillo J, Pinsky M R. Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care. Crit. Care Med. 2001 July; 29(7):1303-10). Given the proven benefit of early resuscitation in sepsis, there is additional imperative to develop methods to diagnose infection earlier with potential to save lives.
In another example, neutropenia is an intended iatrogenic side effect of myeloablative chemotherapy, commonly employed in the management of malignant hematological diseases, most commonly leukemia and lymphoma. Consequently, the host's immune system is compromised leading to increasing risk of opportunistic infections (Neth O W, Bajaj-Elliott M, Turner M W, Klein N J. Susceptibility to infection in patients with neutropenia: the role of the innate immune system. Br J. Haematol. 2005 June; 129(6):713-22). Febrile illness during neutropenia is often the first indication of infection. It requires prompt antimicrobial therapy with possible hospitalization. Thus, depending on therapy, neutropenic patients experience a variable risk of fever, but when fever occurs, it is synonymous with infection in the majority of patients.
Prognosis of neutropenic infection is largely dictated by the severity of the systemic inflammatory response syndrome (SIRS) and clinical progression to sepsis syndrome, severe septic shock and organ failure, with increasing risk of death. Overall, febrile neutropenic patients admitted to the intensive care unit with systemic inflammatory response syndrome display a mortality risk of 20%, increasing to 90% in the presence of septic shock (Regazzoni C J, Khoury M, Irrazabal C, Myburg C, Galvalisi N R, O'Flaherty M, et al. Neutropenia and the development of the systemic inflammatory response syndrome. Intensive Care Med. 2003 January; 29(1):135-8) Regression analysis demonstrated that mortality was not modified by age, malignancy or positive blood cultures, highlighting the importance of the host response in determining outcome. These results underscore the importance of early diagnosis and early identification of severity of illness in the management of febrile neutropenic patients.
Complex systems are systems comprised of a dynamic web of a large and variably interconnected number of elements. Arising from the complex interconnection of the parts (e.g. bees, neurons) and their environment (i.e. non-equilibrium), a new entity called a complex system (e.g. beehive, CNS) arises possessing distinct systemic or emergent properties (e.g. capacity to make honey, cognition, memory). Given that systemic properties are wholly distinct from the properties of the parts, complex systems cannot be fully understood solely by understanding their parts, no matter how thorough that understanding may be (Gallagher R, Appenzeller T. Beyond Reductionism. Science. 1999; 284:79) Given convincing evidence as well as promising insights, it has been observed that the host response to severe infection or injury, which may lead to organ dysfunction, is indeed a complex non-linear system (Seely A J, Christou N V. Multiple organ dysfunction syndrome: exploring the paradigm of complex nonlinear systems. Crit. Care Med. 2000 July; 28(7):2193-200).
Identifying the host response to severe insult as a complex system helps explain why unpredictable rapid deterioration in patients with infection and unexpected clinical improvement with no identifiable cause, both occur frequently, as uncertainty and surprise are ubiquitous within complex systems. If critical illness is characterized by an altered and unpredictable complex systemic response, then there is an imperative to monitor the whole system as a whole and do so over time, in order to track the trajectory of the system. As temporal variability of the parts is produced from the integrity and complexity of the whole system, then it has been hypothesized that continuous monitoring of variability offer means to monitor the whole system over time (Seely and Christou).
The science of characterizing rhythms, referred to most commonly as variability analysis, represents the means by which a time-series of a biologic signal is comprehensively characterized, utilizing an array of linear and non-linear variability analysis techniques based upon non-linear dynamics, chaos theory and mathematical physics (Seely A J, Macklem P T. Complex systems and the technology of variability analysis. Crit. Care. 2004 December; 8(6):R367-84). Each technique provides different and complementary means to characterize patterns of variation. Within a complex systems paradigm, variability analysis offers technology to more directly monitor the underlying system producing the dynamics.
A variety of techniques exist to quantify and characterize variation over time, including Time Domain, Frequency Domain, Entropy, and Scale-Invariant Analyses. Briefly, Time Domain analysis involves the raw data measured over time, an analysis of overall variation (standard deviation and range) and the degree to which data may be fit by standardized distributions (e.g. normal, log-normal). Frequency Domain analysis evaluates the frequency spectrum of a signal observed over time. Any time series may be represented as a sum of regular oscillations with distinct frequencies, conversion from a time domain to a frequency domain analysis (and back) is made possible with a mathematical transformation called the Fourier transform. Wavelet Analysis combines time and frequency domain variation information, providing a hybrid of time- and frequency-domain analysis. Entropy Analysis provides a measure of the degree of information, irregularity, disorder or complexity within a biologic signal. Mathematical calculations produce single (e.g. approximate or sample entropy) or multiple values (e.g. multiscale entropy) that reflect degree of irregularity or complexity. Scale-invariant Analysis provides a measure of common patterns of variation present across all time scales.
This panel of variability analysis techniques was developed to help characterize biologic signals. They have been applied to heart rate, respiratory rate, blood pressure, neutrophil count, temperature and more; investigations have consistently demonstrated the following: (1) patterns of variability provide additional clinically useful information regarding the absolute value of that parameter, (2) altered variation is present in association with age and illness, and (3) degree of alteration correlates with severity of illness.
A reduction in heart rate variability (HRV) has long been utilized as a means to identify fetal distress, as well as a marker of mortality risk in adult patients with heart disease. More recently, HRV evaluation has been performed in the presence of infection, demonstrating reproducible alteration in HRV in patients with sepsis, septic shock and organ dysfunction. Of value to intensivists, the degree to which HRV is altered in the presence of infection correlates with severity of illness. The results of many recent studies strongly support the hypothesis that altered HRV provides an untapped means of early identification of infection in adults.
In another environment, Multiple Organ Dysfunction Syndrome (MODS), defined by having two or more failing organ systems, is the clinical syndrome characteristic of the chronically, critically ill patients. MODS is the leading cause of mortality in intensive care unit (ICU) patients. MODS represents the sequential deterioration of organ function, usually leading to death, occurring in patients who are on the most advanced ICU life support technology possible. These patients require considerable human and hospital resources, including invasive monitoring in an ICU, one-on-one nursing, multiple transfusions, ventilators, dialysis, cardiac assist devices, vasopressors and more.
Evaluation of variability of patient parameters has only recently come under investigation in medical science, and is generally not used in routine clinical practice. As discussed above, variability describes the degree and character to which a parameter fluctuates over time. It is a principal component of the dynamics of a variable, which refers to its pattern of change over time. A parameter may be relatively constant, demonstrating a low degree of variability, or wildly fluctuate with high variability, or demonstrate decreased irregularity or complexity, or decreased high frequency variability.
Generally, reduced variability and complexity are correlated with illness state, however, both increased and decreased variability of individual patient parameters are associated with disease states. The positive clinical significance of the evaluation of these individual variables indicates that the evaluation of multiple patient parameters will provide for clinically useful information.
U.S. Pat. No. 7,038,595 to Seely, published May 2, 2006, describes a system for multiple patient parameter variability analysis and display. The system described in Seely, provides analysis and display of the variability of multiple patient parameters monitored by bedside monitors for each patient over time. Each monitored patient parameter is measured in real-time, data artefacts can be removed, and variability analysis is performed based upon a selected period of observation. Variability analysis of each interval of time yields variability of the patient parameters, which represents a degree to which the patient parameters change over an interval time, to provide diagnostic information particularly useful in the detection, prevention, and treatment of MODS among other uses.
Although such a system provides clinicians with variability data of multiple patient parameters simultaneously, along with the capability for variability analysis over time, there as yet exists no complete solution for organizing use of the acquired data, in particular aside from configurations in the ICU environment, or for conveniently handling data from multiple acquisition sites.